Privacy-Accuracy Consideration in Devices That Collect Sensor-Based Information.

Area of Science:

• Human-Computer Interaction
• Information Security
• Consumer Electronics

Background:

• Smart devices enhance user satisfaction through personalized support.
• High accuracy in support requires access to private user data, creating privacy risks.
• Users may avoid devices perceived as a privacy threat.

Purpose of the Study:

• To analyze the trade-off between device accuracy and user privacy.
• To model user utility based on privacy risk and support quality.
• To propose an optimal policy for smart device data exploitation.

Main Methods:

• Developed an analytical model of the privacy-accuracy trade-off.
• Assumed positive correlations between user utility, privacy risk, and support quality.
• Utilized three analytical propositions to derive an optimal policy.

Main Results:

• User utility is positively correlated with both privacy risk and advice quality.
• Privacy risk extent is unknown to users, influencing adoption decisions.
• Initial limited data exploitation by devices can mitigate user apprehension.

Conclusions:

• An optimal policy for smart device data exploitation is proposed.
• Balancing privacy concerns with accuracy is crucial for user adoption.
• Phased data utilization can enhance user trust and device interaction.